Note: Descriptions are shown in the official language in which they were submitted.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
1
A METHOD AND SYSTEM FOR ANALYSING A MOBILE
OPERATOR DATA NETWORK
FIELD
[0001] The present method and system generally relate to analysis
of mobile operator data network. More specifically, the present method and
system analyses amongst other things relative portfolio share of mobile
devices
with data capabilities, based on real time information extracted from a Mobile
Operator data network. Additionally, the impact of specific features of the
mobile devices, including among others the operating system and data rate,
are also analyzed. The present method and system offer a snapshot of the
portfolio shares at a given time, or their evolution over a specific duration.
Furthermore, the usage of mobile data services is compared between different
models of mobile devices.
BACKGROUND
[0002] The competition between Mobile Operators is becoming
increasingly intense and complex, especially with the advent of advanced
mobile data services offering multiple opportunities to differentiate and
compete
amongst Mobile Operators.
[0003] Each Mobile Operator needs to implement strategies to
maintain or even increase the number of its subscribers and the Average
Revenue Per User (ARPU). One way to do this is to introduce new mobile
devices, with characteristics and capabilities that are expected to appeal to
current subscribers and potential new subscribers. Furthermore, mobile
devices with advanced capabilities for mobile data services are considered as
a
good incentive to boost the ARPU.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
2
[0004] The most common types of mobile data services offered over
mobile IP networks include web browsing and e-mails. However, for corporate
subscribers, advanced mobile data services offerings, with almost the same
level of functionalities on the move, compared to those available at the
office,
are proposed by Mobile Operators. These functionalities include Virtual
Private
Networks (VPN), access to corporate productivity applications, on-line
collaboration, and secure e-mail access. For subscribers interested in fancy
multimedia capabilities, a whole set of services including music delivery,
video
delivery, television, social networking, on-line gaming, are supported by the
latest generation of mobile devices.
[0005] In this context, a Mobile Operator may decide to distribute a
specific mobile device, with a set of features expected to support the Mobile
Operator strategy in terms of consumer gains or ARPU increase. The device
form factor and the strength of its manufacturer brand are also very important
parameters to take into account. The Mobile Operator may even consider
having the exclusivity on a highly popular mobile device, to further increase
its
impact, by making it available to its subscribers only. Alternatively, the
Mobile
Operator may also select various mobile devices from different manufacturers,
with specific characteristics that have been identified as a must have, in the
context of the delivery of advanced mobile data services.
[0006] A critical point for the Mobile Operator is the ability to assess
the impact of a specific marketing strategy, for instance the launch of a new
high-end mobile device. Generally speaking, the Mobile Operator would benefit
from having metrics to track the evolution of the portfolio share of various
mobile devices on a regular (daily, weekly, monthly) basis. Using historic
data,
it would be interesting also to better understand the impact of the
introduction
of former mobile devices, in order to anticipate the impact of new mobile
devices with similar characteristics.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
3
[0007] Another critical point for the Mobile Operator is the ability to
analyze the impact of specific models of mobile devices on the mobile data
services consumption: compare usage of a selected list of mobile data services
(in terms of volume of data exchanged, number of unique subscribers using the
service, frequency of use) for different models of mobile devices. For this
purpose, it is necessary to memorize over time the mobile data services
consumption of the subscribers, to memorize the models of mobile devices
used by the subscribers, and to perform a correlation between the mobile data
services consumed and the models of mobile devices used for this purpose.
[0008] Currently, a Mobile Operator only has a static and partial view
of the respective portfolio shares of various data enabled mobile devices
using
its network. For instance, the information system of the Mobile Operator keeps
track of the mobile devices which have been purchased by its subscribers
directly from the Mobile Operator. However, it does not take into account the
mobile devices purchased from other sources (usually referred to as the grey
market), resulting in the Mobile Operator not knowing which mobile device is
used by some subscribers. Also, roaming users are not taken into account by
the aforementioned information system. Thus, the Mobile Operator information
system may not take into account the mobile devices using the mobile data
network, for a percentage of users as high as ten or even twenty percent of
the
total number of users. In this case, the metrics based on the Mobile Operator
information system could be at best approximate, and even totally inaccurate.
[0009] Another drawback of the data that is extracted from the
Mobile Operator information system is that it is static. Knowing that a
subscriber purchased a specific mobile device is not sufficient. It gives no
information on when it is effectively used on the Mobile Operator data
network.
Also, when a subscriber changes its mobile device, the information system of
the Mobile Operator does not keep track of the previously used mobile device.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
4
[0010] The last point is that the data that can be extracted from an
information system varies greatly in terms of format, completeness, from one
Mobile Operator to another. This would make it difficult to have a generic
analytic system performing the type of portfolio share analysis mentioned
before. Some customization would be necessary for each Mobile Operator, to
interface a generic analytic system with its proprietary information system.
Also,
to have a good granularity, information would have to be extracted at least on
a
daily basis, which may add additional constraints on the information system.
[0011] Therefore, there is a need of overcoming the above
discussed issues concerning the availability of exhaustive, real time data.
Accordingly, a method and system for analyzing mobile devices portfolio share
on a Mobile Operator data network are sought.
[0012] An object of the present method and system is therefore to
analyze mobile devices portfolio share on a Mobile Operator data network.
Another object is to keep track and use the history of a subscriber in terms
of
owned mobile devices to generate interesting metrics and to evaluate portfolio
share gains and losses.
[0013] The foregoing and other objects, advantages and features of
the present method and system will become more apparent upon reading of the
following non-restrictive description of any illustrative embodiments thereof,
given by way of example only with reference to the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In the appended drawings:
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
[0015] Figure 1 illustrates a method and system for analyzing mobile
devices portfolio share on a Mobile Operator data network, according to a non-
restrictive illustrative embodiment;
[0016] Figure 2 illustrates a type of report that is generated by the
analytic system performing mobile devices portfolio share analytics, according
to a non-restrictive illustrative embodiment;
[0017] Figure 3 illustrates another type of report that is generated by
the analytic system performing mobile devices portfolio share analytics,
according to a non-restrictive illustrative embodiment;
[0018] Figure 4 illustrates another type of report that is generated by
the analytic system performing a correlation between mobile data services
usage and models of mobile devices, according to another non-restrictive
illustrative embodiment;
[0019] Figure 5 illustrates the system architecture of the analytic
system performing mobile devices portfolio share analytics, according to a non-
restrictive illustrative embodiment.
DETAILED DESCRIPTION
[0020] In a general embodiment, the present method is adapted for
analyzing a mobile operator data network. For doing so, the method
dynamically collects information of mobile devices from Internet Protocol data
sessions occurring on the mobile operator data network. The method records
the collected information in a database, and processes the collected
information to detect at least one change in one of the mobile devices. Then,
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
6
the method records the at least one change for the corresponding mobile
device and a date of occurrence. Then method further analyses the collected
information and the recorded at least one change to generate metrics
representative of evolution on the mobile operator data network.
[0021] In another general embodiment, the present system is
adapted for analyzing a mobile operator data network. For doing so, the
system comprises a pre-processing unit, a database and an analytic engine.
The pre-processing unit is adapted for receiving dynamically collected
information of mobile devices from Internet Protocol data sessions occurring
on
the mobile operator data network. The pre-processing unit further detects at
least one change in one of the mobile devices and records the at least one
change for the corresponding mobile device with a date of occurrence. The
database is adapted for recording the collected information, the at least one
change for the corresponding mobile device and the date of occurrence. The
analytic engine is adapted for analyzing the collected information and the
recorded at least one change to generate metrics representative of evolution
on
the mobile operator data network.
[0022] Generally stated, a non-restrictive illustrative embodiment of
the present is a method and system to generate metrics related to the type of
mobile devices used on a Mobile Operator data network. The goal of these
metrics is to help the Mobile Operator better follow the portfolio share of a
specific mobile device model, a group of mobile devices models, or a
manufacturer. The metrics can also focus on specific characteristics of data
enabled mobile devices. For instance, following the evolution of the portfolio
share of mobile devices with a given operating system, a given data rate, a
given form factor, etc.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
7
[0023] Additionally, a method and system according to a non-
restrictive illustrative embodiment of the present relies on a filtering
system for
extracting real time information from the Mobile Operator data network. The
information consists essentially in reporting the model of the mobile device
used by a subscriber performing a data session. This information is
transmitted
to a centralized analytic system. The filtering system relies on Deep Packet
Inspection (DPI) technologies or any other similar technology, which has the
capability to extract relevant information directly from Internet Protocol
(IP)
based data sessions of active subscribers.
[0024] Furthermore, a method and system according to a non-
restrictive illustrative embodiment of the present relies on an analytic
system to
process, memorize and analyze the information transmitted by the filtering
system. The analytic system records the historic of the models of mobile
devices used by the subscribers. The analytic system also computes metrics
related to the portfolio share of the models of mobile devices used on the
Mobile Operator data network.
[0025] Moreover, a method and system according to a non-
restrictive illustrative embodiment of the present enables a correlation of
the
mobile data services usage with models of mobile devices used by subscribers.
For this purpose, the filtering system also extracts real time information
related
to the mobile data services usage of the subscribers and transmits them to the
analytic system. The analytic system memorizes this information, and
computes metrics to correlate the mobile data services usage with the models
of mobile devices.
[0026] Also, a method and system according to a non-restrictive
illustrative embodiment of the present allow for presenting the metrics to the
Mobile Operator in the form of customizable reports. These reports give a
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
8
snapshot of the portfolio share of the selected items at a given time. The
reports also provide the evolution of the portfolio share of the selected
items
over a given period, and a correlation between the mobile data services usage
and the models of mobile devices.
[0027] The reports generated by the present method and system
enable Mobile Operators to follow trends, knowing which mobile devices have a
growing popularity and which have a declining popularity. Also the
attractiveness of specific capabilities of advanced data enabled mobile
devices
can be evaluated. These are powerful tools to help Mobile Operators offer the
kind of mobile devices that have a positive impact on subscriber retention /
gain, and also on mobile data ARPU increase. Furthermore, the history of a
subscriber in terms of owned mobile devices can be used to generate
interesting metrics, to evaluate portfolio share gains and losses.
[0028] Figure 1 illustrates a method and system for analyzing mobile
devices portfolio share on a Mobile Operator data network.
[0029] A mobile network 50 owned by a specific Mobile Operator is
considered in Figure 1. Examples of such mobile networks include cellular
networks implementing one of the following standards: General Packet Radio
Service (GPRS), Universal Mobile Telecommunication System (UMTS), Code
Division Multiple Access 2000 (CDMA 2000), and the future Long Term
Evolution (LTE) standard. Worldwide Interoperability for Microwave Access
(WIMAX) networks are another type of mobile networks that can be considered.
The mobile network 50 is usually operated over a whole country, but could also
cover a specific administrative region or geographic area in one or several
countries.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
9
[0030] Subscribers use different types of mobile devices 10, 12, 14
to operate on the mobile network 50. Each mobile device has two related
characteristics: its manufacturer and a specific model within the manufacturer
product range.
[0031] The mobile network 50 comprises a mobile data network 60,
to transport the data traffic generated by the mobile data services provided
by
the Mobile Operator. Such mobile data services consist, among others, in web
browsing, e-mail, multimedia delivery, social networking, on-line gaming,
corporate mobile data applications, etc. The Internet Protocol (IP) is the
underlying networking protocol used in mobile data networks, in the case of
any
type of cellular network as well as for WIMAX networks.
[0032] A filtering system 110 is connected to the mobile data
network 60 and has the capability to capture the IP traffic generated by data
sessions of the mobile devices 10, 12, 14. The filtering system 110 is based
on
a technology well known in the art: Deep Packet Inspection (DPI). DPI consists
in capturing IP based data traffic, analyzing the different IP protocol layers
(network, transport, session, application ...), and extracting relevant
information
from these protocol layers. The filtering system 110 is deployed in a
strategic
location of the mobile data network 60: a place where all IP based data
sessions converge and are aggregated, before accessing external IP networks
like the Internet. This location is usually referred to as the IP Core Network
of
the Mobile Operator, by contrast to the Radio Access Network. The advantage
of deploying the filtering system in the IP Core network is that one to a few
instances will be sufficient to monitor all the IP based data traffic. By
comparison, deploying the filtering system in the Radio Access Network would
require hundreds and even thousands of instances.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
[0033] To illustrate how the filtering system 110 operates, details will
now be provided in the case of a UMTS cellular network. For a UMTS cellular
network, the best point of capture for the filtering system 110 is the Gn
interface
of the Gateway GPRS Support Node (GGSN). Each (incoming or outgoing)
data session goes through the Gn interface. The GPRS Tunneling Protocol
(GTP) is used to transport the IP based data sessions of the subscribers on
the
Gn interface of the GGSN. The GTP protocol has a user plane to transport the
IP based data and a control plane to manage the data sessions of each
subscriber. A unique identifier of the mobile device used by the subscriber
can
be extracted from the GTP control plane: the International Mobile Equipment
Identity (IMEI). This identifier can be used to identify the manufacturer and
model of the mobile device: the IMEI is composed of a sub-section identifying
the manufacturer, a sub-section identifying the specific model within the
manufacturer portfolio of mobile devices, and a sub-section identifying the
specific mobile device owned by the subscriber. Additionally, a unique
identifier
of the subscriber can be extracted from the GTP control plane: the
International
Mobile Subscriber Identity (IMSI). Thus, for each IP based data session on the
Gn interface of the GGSN, the filtering system 110 uses its DPI capabilities
to
extract the related IMEI and the IMSI. This information is transmitted to an
analytic system 100, with a timestamp indicating the date and time of the
session.
[0034] Alternatively, the Gi interface of the GGSN can be used for a
UMTS cellular network. In this case, the IMEI and IMSI related to an IP based
data session can be extracted from Remote Authentication Dial In User Service
(RADIUS) messages used for authentication, authorization and accounting
purposes. The filtering system 110 analyzes the RADIUS messages to extract
the relevant information. In the RADIUS messages, the IMSI may not be
available, in which case it is replaced by the Mobile Subscriber ISDN (MSISDN
- mobile phone number), to uniquely identify the subscribers.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
11
[0035] Although the filtering system 110 has been described in the
context of a UMTS network, the principles of operation may be generalized to
any type of mobile network. For each IP based data session, an identifier of
the
mobile device being used and an identifier of the subscriber are extracted. An
identifier of the subscribers (like the IMSI or MSISDN in the case of an UMTS
network) is always present in the IP based data sessions (monitored by the
filtering system 110), since it is a critical information for the
authentication,
authorization and billing of the subscribers. Regarding the identifier of the
mobile devices (like the IMEI in the case of an UMTS network), it is also
always
present in the IP based data sessions (monitored by the filtering system 110).
In the case of cellular networks like UMTS, CDMA2000, and LTE, it is the IMEI
or an equivalent. In the case of a WIMAX network, it is the Media Access
Control (MAC) address of the terminal. In both cases (IMEI or MAC address),
the manufacturer and model of a mobile device can be extrapolated from this
identifier.
[0036] Reverting to Figure 1, assuming that all the mobile devices
represented on Figure 1 are engaged in a data session, the filtering system
110
reports the following information to the analytic system 100: seven different
subscribers have performed a data session, three of them using the mobile
device of model 10, three of them using the mobile device of model 12, and
one of them using the mobile device of model 14. As mentioned before, the
identifier of each subscriber and a timestamp for each data session are
transmitted as well.
[0037] The analytic system 100 receives the information extracted
by the filtering system 110 on a regular basis, for instance every day or
every
week, based on the Mobile Operator needs. In a typical deployment, a single
instance of the analytic system 100 is in operation. It may be necessary to
deploy several filtering systems 110, at different points of capture in the
mobile
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
12
data network 60. In this case, the information reported by the different
filtering
systems 110 is aggregated by the analytic system 100.
[0038] As illustrated in Figure 5, the analytic system 100 comprises
a pre-processing unit 510 and a database 520. The pre-processing unit 510 is
in charge of receiving the data from the filtering system(s) 110, processing
this
data, and updating the database 520 when necessary. The database stores
amongst other things, the evolution of the models of mobile devices used by
each subscriber of the Mobile Operator over time.
[0039] The data received by the pre-processing unit 510 consists in
a flat file, each entry of the flat file containing: a subscriber identifier,
a mobile
device identifier, and a timestamp. Each entry of the flat file corresponds to
an
IP based data session monitored by the filtering system 110 of Figure 1, as
explained previously. For each entry in the flat file, the pre-processing unit
510
extracts from the database 520 the identifier of the model of mobile device
currently in use for the subscriber identified by the subscriber identifier in
the
flat file entry. If the identifier of the model of mobile device in the flat
file entry
differs from the one extracted from the database, the pre-processing unit
infers
that the subscriber has changed its mobile device. Thus, the pre-processing
unit updates the database with the identifier of the new model of mobile
device
used by the subscriber, with the related timestamp to identify the date at
which
the update occurred. As previously mentioned, the mobile device identifier is
composed of a sub-part identifying the manufacturer and a sub-part identifying
a precise model within the manufacturer portfolio. The pre-processing unit
also
updates the database with the name of the manufacturer and the name of the
model associated to the identifier of the mobile device. The pre-processing
unit
510 uses the identifiers of the mobile devices to query the database 520,
while
an analytic engine 530 uses the names of the manufacturers and the names of
the models for its queries to the database 520. Since the analytic engine 530
is
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
13
controlled by the end users via an end user control interface 550, the
identifiers
of the mobile devices cannot be used, because they have no meaning for the
end users, who only understand the names of the manufacturers and the
names of the models.
[0040] The correlation between the names and the identifiers of the
mobile devices is obtained from external sources, usually the mobile devices
manufacturers or third party suppliers. The correlation data is stored in the
pre-
processing unit 510 or in the database 520, and is updated regularly with the
correlation data for the new mobile devices which appear on the market. The
update is performed manually by a system administrator, or is automated if a
reliable source can be automatically queried to obtain the information.
[0041] For each subscriber of the Mobile Operator data network, the
database 520 keeps track of the currently used model of mobile device, and
also records the previously used models. The database 520 contains all the
subscribers to the Mobile Operator data network, and is updated when new
subscribers register with the Mobile Operator data network. The database 520
may be a dedicated database specifically put in place for the purpose of the
present method and system, or an existing database containing information on
all the subscribers extended to support the functionality of the present
method
and system. The index used to identify a specific subscriber in the database
520 is the unique identifier of the mobile devices collected by the filtering
system 110 (for example, the IMSI or the MSISDN for an UMTS cellular
network).
[0042] An algorithm is implemented in the pre-processing unit 510,
to detect a transition between a previous and a new model, in case the
subscriber is still using both mobile devices for a limited duration. The
objective
is to record in the database 520 only effective changes of mobile devices, and
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
14
to detect temporary flip-flops between a previous and a new model. The
algorithm can also be used to detect the case where one subscriber has two
different mobile devices, for instance one for its work and one for its
personal
use.
[0043] Optionally, the analytic system 100 may also keep track of
the roaming mobile devices present on the Mobile Operator data network 60.
The filtering system 110 captures the identifiers of the roaming mobile
devices
(and the identifiers of their models of mobile devices) in the same manner as
the identifiers of the mobile devices subscribed to the Mobile Operator data
network. The first time a roaming mobile device is detected on the mobile
operator data network 60, the pre-processing unit 510 queries the database
520 and obtains no answer for the identifier of the roaming mobile device. It
infers that the mobile device is a roaming mobile device. Upon this first
detection of the roaming mobile device, the pre-processing unit 510 adds the
roaming mobile device to the database 520, with a specific flag indicating it
is
roaming. It also records the identifier of the model of mobile device that the
roaming mobile device is currently using. After this operation, the roaming
mobile device is treated as a mobile device subscribed to the mobile operator
data network 60. If the roaming mobile device is detected again later on the
mobile operator data network 60, the pre-processing unit 510 is capable of
identifying the latter by interrogating the database 520 with its identifier.
This is
a means of having reliable statistics on the roaming mobile devices: if a
roaming mobile device is detected consecutively five times on the mobile
operator data network with the same model of mobile device, a single instance
of the model of mobile device is recorded in the database 520 in relation to
this
specific roaming mobile device. Consequently, the analytic engine 530
represented on Figure 5 generates metrics related to the mobile devices
portfolio share, taking into consideration the subscribers of the Mobile
Operator
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
data network only, the roaming mobile devices only, or a combination of the
roaming mobile devices and the subscribed mobile devices.
[0044] The analytic system 100 generates metrics related to the
evolution of the models of mobile devices used on the mobile data network 60.
Specifically, the analytic engine 530 represented on Figure 5 is the entity
responsible for computation of the metrics. These metrics are further
processed
to generate reports, which are presented to the Mobile Operator via a
Graphical
User Interface. An example of such reports consists in a dashboard comparing
the portfolio share evolution of several pre-selected models of mobile
devices.
The metrics and the associated reports will be further detailed when
describing
Figure 2.
[0045] To generate the metrics, data are extracted from the
database 520, aggregated when needed, and some computation is performed
to obtain the final metric. One option is to have the database 520 perform the
three operations (extraction, aggregation, computation) under the control of
the
analytic engine 530. Alternatively, the database 520 only performs extraction
and basic computations, while the aggregation and more sophisticated
computations are performed by the analytic engine 530. Two types of metrics
are generated: static and dynamic.
[0046] Following is an example of a static metric and how it is
generated by the analytic engine 530. The metric considered is the portfolio
share of each manufacturer in percentage, at a specific day. Every night, the
analytic engine 530 computes this metric for the previous day and stores the
result in the database 520. To compute the metric, the analytic engine 530
generates requests to the database 520 to calculate the total number of mobile
devices for each manufacturer, for the day considered. The analytic engine 530
transforms the numbers for each manufacturer in a percentage of the total
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
16
number of mobile devices and the resulting metrics are stored in the database
520 with a timestamp to identify the day at which the computation has been
performed. Additionally, the request to the database 520 may include a
parameter to perform the computation for the mobile devices subscribed to the
Mobile Operator data network 60 only, for the roaming mobile devices only, or
for the combination of the subscribed and roaming mobile devices. Reports
based on this metric are generated on demand (for the end users) by the
analytic engine 530. For example, the analytic engine 530 extracts from the
database 520 the metrics for a given day, to present a report with the
portfolio
share of each manufacturer expressed in percentage for the day in question. In
another example, the analytic engine 530 extracts from the database 520 the
metrics for a subset of manufacturers for each consecutive days representing a
period of time (for instance a month), to present a report with the comparison
of
the evolution day-by-day of the portfolio share of the selected manufacturers
over the selected period of time. Another static metric is the portfolio share
of
each specific model of mobile device in percentage, at a specific day. This
metric is generated using the same principles as for the manufacturer
portfolio
share metric.
[0047] A dynamic metric is a metric that is not part of the pre-defined
metrics supported by the analytic engine 530. It is computed to generate an ad-
hoc report defined dynamically by an end user. The dynamic metric does not
benefit from intermediate computations performed every day by the analytic
engine 530, as described for a static metric. All the operations necessary to
generate the metric (extraction, aggregation, computation) are executed in
real
time. Thus, such a dynamic metric is usually more demanding in terms of
processing power and requires a longer delay to be generated. An example of
a dynamic metric is the portfolio share (in absolute value and in percentage)
of
all mobile devices with a WIFI connection (assuming that this metric has not
been included in the list of static metrics computed every day by the analytic
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
17
engine). For demonstration purposes, upon receipt of a request from an end
user for a report showing the evolution of this metric on a three months
period,
the analytic engine 530 sends a request to the database 520, to calculate the
number of mobile devices with a WIFI connection for every day in the three
months period, and also to calculate the percentage of mobile devices with a
WIFI connection reported to the total number of mobile devices for every day.
Then, the analytic engine 530 generates a report with the calculated metric
(absolute value and percentage) for each day in the three months period, to be
presented to the end user.
[0048] The metrics included in the list of static metrics are defined by
the Mobile Operator. The analytic engine 530 is configured with this list of
static
metrics. The static metrics represent information needed to follow the
evolution
of the mobile devices portfolio share, and are requested on a regular basis by
the end users of the analytic system 100, in the form of reports. The reports
are
presented by the report presentation unit 540 to the end users via a Graphical
User Interface. Dynamic metrics are included in ad-hoc reports, and are more
rarely requested by the end users of the analytic system 100 (it is not
possible
to anticipate all the metrics which may be generated by combining the
information present in the database 520). However, a dynamic metric may be
added to the list of static metrics, if the end users decide over time that it
has
become required information. Figure 2 and Figure 3 illustrate exemplary
reports.
[0049] For each model of mobile device that can potentially be
detected on the mobile data network 60, the analytic system 100 has a
description of its characteristics and features. These are used to generate
additional metrics (like the previous example of a dynamic metric based on the
availability of a WIFI connection on the mobile devices). For instance,
description of mobile device characteristics and features may include one or
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
18
several of the following: the operating system, the maximum data throughput,
the form factor, the web browser, etc. These characteristics and features are
criteria that influence the portfolio share of mobile devices; especially for
high
end devices designed to stimulate access to various types of advanced mobile
data services. These characteristics and features are stored in the database
520 and used by the analytic engine 530 to generate the additional metrics.
Thus, the database 520 is updated constantly with the characteristics and
features of the mobile devices that appear on the market. This can be
performed via a manual upgrade. Alternatively, an external data source (500 on
Figure 5) with this type of information can be automatically queried on a
regular
basis by the pre-processing unit 510, to perform the necessary updates to the
database 520.
[0050] Additionally, the analytic system 100 may be interfaced with
an information system 120 of the Mobile Operator. This option is nice to have
but the analytic system 100 shall be able to operate without it. However, some
demographic information related to the subscribers of the subscribed mobile
devices may be extracted from the information system 120 and used by the
analytic engine 530, to correlate the portfolio share metrics with demographic
information. For example, the portfolio share of different models of mobile
devices may be analyzed, taking into account the gender, the age, the social
category, the place of residence, of the subscribers. From an operational
point
of view, one way to proceed is to have the pre-processing unit 510 retrieve
the
demographic information from the information system 120 of the Mobile
Operator (represented as an external data source 500 on Figure 5) and load
this demographic information in the database 520, to be queried by the
analytic
engine 530. This is an iterative process which is repeated on a regular basis,
to
take into account changes in the demographic information of existing
subscribers, and to take into account new subscribers who have been added to
the database 520.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
19
[0051] Another important aspect of the invention is the correlation of
the mobile data services usage with the models of mobile devices. The
filtering
system 110 captures the IP traffic generated during the data sessions of the
multiple mobile devices 10, 12, 14 represented on Figure 1. The following
information is extracted from the data sessions and transmitted to the
analytic
system 100 (more specifically to the pre-processing unit 510 of Figure 5): the
identifier of the subscriber (for example the IMSI in the case of an UMTS
network), the type(s) of mobile data services used during the data session, a
timestamp identifying the beginning of each mobile data service usage, the
volume of data transferred for each mobile data service, etc.. Additional
information characterizing the mobile data services may be added if required.
[0052] The types of mobile data services are obtained via the
classification capabilities of the DPI engine of the filtering system 110. The
DPI
engine recognizes the type(s) of mobile data service(s) among a pre-defined
set of types. Examples of such types include: browsing, messaging, video or
audio streaming, on-line gaming, social networking, Voice over IP (VoIP),
corporate application, etc. The DPI engine analyzes the different IP protocol
layers (network, transport, session, application...) of the captured IP data
sessions and uses signatures to recognize a specific type of application (web
browsing, Skype, Google Mail ...), which is associated to one of the pre-
defined
types of mobile data services. However, for a given type of mobile data
service,
like for example VoIP, different types of VolP applications are detected by
the
DPI engine of the filtering system 110. The present method and system are
based on the detection of the types of mobile data services by the filtering
system 110 and the analysis by the analytic system 100 of these types in
relation to the models of mobile devices. However, if a higher level of
granularity is required, such granular information may be extracted by the
filtering system 110 and analyzed by the analytic system 100 in a similar
manner as previously described.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
[0053] The pre-processing unit 510 of Figure 5 receives the
information collected by the filtering system 110 and stores this information
in
the database 520. For each mobile data service session, the pre-processing
unit 510 receives the following information from the filtering system 110: the
subscriber identifier, the type of mobile data service, a timestamp
identifying
the beginning of the session, the volume of data transferred during the
session,
etc. The database 520 is updated with this information based on the subscriber
identifier.
[0054] The analytic engine 530 generates metrics related to the
correlation of the mobile data services usage with the models of mobile
devices. The computation of the metrics has previously been described and the
principles are similar to those described for the computation of the metrics
related to the mobile devices portfolio share. A metric for the aforementioned
correlation consists in computing the usage for a specific type of mobile data
service, for a specific mobile device, over a specific period of time. The
analytic
engine 530 queries the database 520 to extract the relevant information and
compute one or several values representing the usage for the selected
parameters (type of mobile data service, model of mobile device, period of
time,etc). The values are computed taking into consideration all the mobile
devices subscribed to the mobile data network 60 recorded in the database 520
(as already mentioned, combinations including or excluding roaming mobile
devices can be used). Three examples of types of values to represent the
usage include: volume of data generated by the mobile data service over the
period of reference, number of unique mobile devices or subscribers of mobile
devices accessing the mobile data service over a period of reference, and
frequency of usage of the mobile data service over the period of reference.
These three alternative metrics to evaluate the usage of a mobile data service
will be further detailed in the description of Figure 4. Other metrics related
to
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
21
mobile data service and mobile data usage could further be generated using
the presently described method and system.
[0055] The analytic engine 530 generates reports based on the
computed metrics, to be presented to the end users by the reports presentation
unit 540 of Figure 5. To generate a report, a mobile data service and a period
of reference are selected. Metrics representing the usage are computed for
several models of mobile devices. The metrics are represented on the report,
to
allow the comparison of the usage of the mobile data service between the
different models of mobile devices. The usage metrics related to several types
of mobile data services can also be represented on a single report, for
purpose
of comparison between several services. The usage metrics can also be
calculated per manufacturer (by aggregating the usage of all models of mobile
devices owned by a specific manufacturer). This allows the comparison of the
mobile data services usage between manufacturers of mobile devices, as
depicted in Figure 4.
[0056] Figure 2 illustrates a type of report that is generated by the
analytic system 100 of Figure 1, performing mobile devices portfolio share
analytics.
[0057] The dashboard on Figure 2 represents the portfolio share per
manufacturer. This is the type of information that is used by the Mobile
Operator, to help track the trends in mobile device portfolio share on the
mobile
data network. In the example represented on Figure 2, the portfolio share 210
of manufacturer 1 is the biggest with roughly 40%. It is followed by the
portfolio
share 220 of manufacturer 2 with roughly 25%. It is followed by the portfolio
share 230 of manufacturer 3 with roughly 15%. It is followed by the portfolio
share 240 of manufacturer 4 with roughly 10%. It is followed by the portfolio
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
22
share 250 of the manufacturer 5 with roughly 5%. The portfolio share 260 of
the
remaining manufacturers is roughly 5%.
[0058] A refinement of the portfolio share represented in Figure 2 is
obtained, by extracting the information for each model of mobile device
offered
by the manufacturer. For example, in Figure 2, manufacturer 1 has a portfolio
share 210 of roughly 40%. It is important to know that its top performing
model
owns 10% on its own, the second and third performing models each own
around 5%, and the 20% left are shared among the rest of the models. Based
on this type of information, a dashboard is generated with the top 3
performing
mobile devices for each manufacturer.
[0059] The aforementioned reports can be generated following two
types of time frame. A first type of time frame is a snapshot view of the
selected
portfolio shares. This gives a picture of the targeted portfolio shares at a
given
instant, usually a specific day or week. However, in certain cases, a better
granularity is useful, to follow an outstanding event taking place at a
specific
location (also using additional geographical information extracted by the
filtering system 110 if needed). Using these snapshot views, the Mobile
Operator follows the evolution of the portfolio shares on a regular basis, for
example daily or weekly.
[0060] A second type of time frame is a period of time, like a week, a
month, a year; or any period between two given days. The reports generated
by the analytic system 100 provide the evolution of the respective portfolio
shares of several manufacturers, or of several models (or groups of models) of
mobile devices. The dashboard represented on Figure 3 illustrates this type of
report. The horizontal axis 300 represents the time and the vertical axis 310
the
portfolio share. The evolution of the portfolio share of three models, 350,
360,
370, of mobile devices is represented over the time period. Based on this
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
23
dashboard, the Mobile Operator could draw various conclusions. Model 1, 350,
is a mature mobile device, which portfolio share is declining regularly over
the
time period. Model 2, 360, is a newly introduced mobile device. It was very
popular at the beginning of the period and its portfolio share climbed very
quickly, but it reached a peak rapidly and declined steadily. Model 3, 370, is
also a newly introduced mobile device. This model increased its portfolio
share
at a slow but regular pace, and it seems to have the capability to capture a
significant portfolio share over a long time period.
[0061] A large flexibility is offered in the selection of the
manufacturers or mobile devices for which the reports are generated. Metrics
are calculated by the analytic system 100, for any combination of
manufacturers or models of mobile devices, to generate the appropriate
reports, according to the Mobile Operator's needs. This capability enables the
Mobile Operator to follow the competition between a few models of mobile
devices addressing the same market segment.
[0062] Other metrics that are generated by the analytic system are
related to the dynamics of the portfolio share evolution. For instance, when a
new popular model of mobile device is introduced, a significant number of
subscribers change their current model to adopt this new model. It is very
interesting to track which models are abandoned. The type of dashboard
presented on Figure 2 is used to represent the relative portfolio share losses
per model or manufacturer, caused by the introduction of the new model of
mobile device. Using Figure 2 as an illustration, almost 40% of the
conversions
to the new mobile device affect manufacturer 1, 210; almost 25% affect
manufacturer 2, 220, and so on. Alternatively, the same type of metrics is
applied to a model of mobile device loosing popularity. In this case, a
significant
number of subscribers abandon the model with a declining popularity to adopt a
new model. The type of dashboard presented on Figure 2 is used to represent
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
24
the relative portfolio share gains per model or manufacturer, caused by the
declining popularity of the considered mobile device.
[0063] Several more metrics representing the dynamics of the
evolution of the portfolio share are generated by the analytic system 100. For
example, the model or manufacturer with the highest progression in terms of
gains or the highest progression in terms of losses, related to the portfolio
share, are tracked.
[0064] All the aforementioned metrics addressed the portfolio share
of manufacturers or models of mobile devices, considering different
combinations of the manufacturers and models to generate the metrics and
reports. Alternatively, a specific capability or feature of the mobile devices
is
tracked, to generate the same type of metrics and the associated reports.
[0065] One of these important features is the operating system of
the mobile devices. Its support of advanced multimedia capabilities,
ergonomics, multi-tasking, is critical to offer a good user experience when
consuming mobile data services on the mobile operator data network. There is
a strong competition between the leading operating systems, and they are
more and more considered as an important differentiating factor, particularly
for
the high end mobile devices like the Personal Digital Assistants (PDA). Thus
the evolution of the portfolio share of the main competing operating systems
is
a valuable source of marketing information for the Mobile Operator.
[0066] Another important capability is the available data rate, both
uplink and downlink (the uplink data rate is usually limited compared to the
downlink data rate). A mobile device with a higher data throughput is more
appealing to a subscriber eager to consume advanced mobile data services.
For example for Universal Mobile Telephone System (UMTS) mobile devices,
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
the standard UMTS data rate has been improved with the introduction of
evolutions. One such evolution is the High-Speed Packet Access (HSPA),
which increased the uplink and downlink data rates. Then, HSPA has been
further improved with the so-called HSPA+, to further improve uplink and
downlink data rates The next evolution is 4G with the so-called Long Term
Evolution (LTE), which will again significantly improve uplink and downlink
data
rates. The Mobile Operator may be interested to know the relative portfolio
shares of mobile devices with standard UMTS, HSPA, HSPA+, LTE (and the
following evolutions), capabilities. An increasing proportion of mobile
devices
with enhanced data throughputs is an opportunity to introduce new mobile data
services requiring higher bandwidth. It is also an indicator that the capacity
of
the Mobile Operator data network should be upgraded soon.
[0067] Another important feature is the form factor of a mobile
device. Today, a large array of designs are available, including bar,
clamshell,
flip, slide, swivel. Following the portfolio share of the various designs is a
good
indicator, to figure out which mobile devices have a greater chance to be
among the most popular ones.
[0068] Other features of the mobile devices can be tracked. For
example, the Internet browser, the e-mail client, or any other differentiating
application related to mobile data services. Several different models of
Internet
browsers, e-mail clients, can be embarked on a model of mobile device. Thus,
their portfolio share can also be analyzed and reports generated. For a given
model of mobile device, the web browser and the e-mail client are pre-
installed,
so that these characteristics are known in advance. However, it is becoming
increasingly easy to modify the software of a mobile device, so that customers
may change the original web browser or e-mail client. In this case, the
filtering
system 110 of Figure 1 is adapted for detecting these characteristics in real
time, to have accurate information for each mobile device.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
26
[0069] Localization information can also introduce new perspectives
on the metrics that are calculated by the analytic system 100. In a particular
aspect of the present method and system, the filtering system 110 of Figure 1
is further adapted for recording a radio cell involved in each data session
reported to the analytic system 100 (along with the subscriber unique
identification and the model of mobile device used). The radio cell is given
as
an example, but any type of real time localization information that can be
reported could alternately be used. The aforementioned metrics may then be
calculated for each radio cell (or group of radio cells representing a
geographical area of interest). Reports are generated to identify, for
example,
the areas where high end mobile devices (with capabilities identified as
susceptible to produce more mobile data traffic or advanced mobile data
services consumption) have the greatest portfolio share. This information is
used to detect areas where the mobile network capabilities should be
upgraded. It is also used to target areas, where advanced localization-based
mobile data services have the best chance to succeed.
[0070] Another type of localization information is the city or province
of residence of the subscribers. This information is used to identify the top
adopters location (represented by city or province) for a new model of mobile
device. The information related to the residence of the subscribers can be
provided by the information system 120 of Figure 1.
[0071] Figure 4 illustrates still another type of report that is
generated by the analytic system 100 of Figure 1, performing a correlation
between the mobile data services usage and the models of mobile devices.
[0072] The dashboard on Figure 4 correlates the activity of different
types of mobile data services with different models of mobile devices. In the
example, two models are compared: a first model 450 and a second model
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
27
460. The horizontal axis represents the types of mobile data services 400. In
the example, browsing 402, messaging 404, streaming 406, on-line gaming
408 are considered. The vertical axis represents the activity 410, for each
type
of mobile data service and each model of mobile device.
[0073] The activity is represented over a period of reference;
typically a year, a month, a week, or a day. The period is selected by the end
user and a report is generated by the analytic system 100. It aggregates the
activity measures reported by the filtering system 110 between the beginning
and the end of the period of reference. If a more real time view is required,
the
granularity may be in hours or even minutes. However, a better granularity
involves more processing from the analytic system 100 and thus requires more
powerful components (the database 520 and the analytic engine 530 of Figure
5). As an example, if the duration selected by the end user to generate the
report is the month of March 2009, then the dashboard represented on Figure 4
represents the cumulative activity of March 2009 for browsing 402, messaging
404, streaming 406 and on-line gaming 408.
[0074] The type of mobile data service 400 could include one or
several of the following: browsing, messaging, streaming (audio and video),
broadcasting (e.g. mobile TV or radio), on-line gaming, social networking,
VoIP,
professional services (e.g. secure e-mail, video-conferencing, productivity
applications), etc. The end user has the capability to select a subset of all
available mobile data services, to be represented on a report of the type
displayed in Figure 4.
[0075] A large flexibility is provided by the analytic system 100 for
the comparison between the models of mobile devices. Two or more models
may be compared within the same report. For simplicity, in Figure 4, only two
models 450 and 460 of mobile devices are represented. Alternatively, a single
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
28
model may be compared against a category including several models. For
instance, a model is compared against all the available models, or against all
the models distributed by its manufacturer. For this purpose, the analytic
system 100 aggregates all the activity measures 410 of the models included in
a specific category, to generate the report.
[0076] Different metrics are used to measure the activity 410
represented on Figure 4. The volume of data transmitted during the data
sessions associated to a type of mobile data service is one possibility.
Alternatively, the average volume of data per mobile data session or any other
data corresponding to usage of the data service could be used.
[0077] A percentage of unique subscribers accessing the mobile
data service over the period of reference is another metric supported by the
analytic system 100 to measure the activity 410. In this context, a unique
subscriber is defined as a subscriber using the mobile data service at least
once over the period of reference. The fact that this subscriber uses the
mobile
data service several times is not relevant (this case is taken into account by
another metric, the frequency, which will be introduced later). For example,
the
streaming data service 406 could be considered over a period of reference of
one day. The activity 410 for the model 450 in terms of percentage of unique
subscribers is the number of subscribers owning the model 450, which have
used at least once the streaming data service during the selected day, divided
by the total number of subscribers owning the model 450, expressed in
percentage. This notion of unique subscriber may not be relevant for mobile
data services used frequently by most subscribers, like browsing. But for a
new
mobile data service recently deployed by a Mobile Operator, it is a very
interesting metric to follow the adoption rate of the service, correlated to
the
model of mobile device.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
29
[0078] Analyzing the dashboard given as example on Figure 4, the
Mobile Operator may discover several trends. The first model 450 is more
extensively used for browsing than the second model 460. On the other side,
the second model is more extensively used for streaming and on-line gaming
than the first one. One can infer that the second model is better suited for
multimedia oriented mobile data services. Additionally, the two models have a
similar usage in terms of messaging and cannot be differentiated with respect
to this type of data service.
[0079] Another type of possible dashboard is the comparison of the
evolution of the activity of a type of mobile data service over a time period
for
different models of mobile devices. The period (week, month, year) over which
the comparison is performed is represented on the horizontal axis. The
vertical
axis represents the activity of the selected type of mobile data service (the
streaming activity for example). The metrics to measure the activity are those
introduced for Figure 4 (volume of data or unique subscribers). The activity
is
represented for two or more models of mobile devices to be compared.
Analyzing this type of dashboard, the Mobile Operator can discover which
models of mobile devices are most likely to boost the consumption of the type
of mobile data service analyzed (for example streaming).
[0080] One additional metric supported by the analytic system 100 is
the frequency. It consists in comparing the frequency of use of a selected
type
of mobile data service between several models of mobile devices, over a
selected time period. For example, considering a time period of one day, the
following frequencies are introduced: once, twice, three to five times, six to
ten
times, and more than then times. For each model of mobile device selected,
the percentage of mobile devices of this model using the mobile data service
(e.g. streaming) at each of the frequencies is calculated. This enables the
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
Mobile Operator to detect which models of mobile devices generate the most
frequent consumption of the mobile data service.
[0081] Reports identifying the most active and most inactive models
of mobile devices are also generated by the analytic system 100. Such a report
compares the activity for a given mobile data service over a selected time
period. As already stated, the activity may be measured in terms of volume of
data, unique subscribers or any other appropriate criteria. Dashboards with,
for
example, the top five active models and the top five inactive models are
displayed.
[0082] The activity for a given mobile data service is also correlated
to specific characteristics of the mobile devices. Such characteristics
include,
among others: the size of the screen, the resolution of the camera, the form
factor, the operating system, the uplink and downlink data rate... For
instance,
the mobile devices are divided into several categories of screen size, and the
activity in term of streaming is compared between these categories. This
comparison is relevant since the size of the screen has an impact on any
multimedia based mobile data service.
[0083] Figure 5 illustrates an embodiment of the system architecture
of the analytic system 100 for performing mobile devices portfolio share
analytics.
[0084] As represented on Figure 5, the analytic system 100
introduced in Figure 1 is composed of the following sub-entities: a pre-
processing unit 510, a database 520, an analytic engine 530, a reports
presentation unit 540, and an end-user control interface 550.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
31
[0085] The analytic system 100 receives data from the filtering
system 110. As already explained, several instances of the filtering system
110
may be deployed in different parts of the mobile data network 60 of Figure 1.
Each instance reports real time data to the analytic system 100. In case the
volume of data to handle is too large, the analytic system 100 may also be
split
between several instances, to scale. Optionally, the analytic system 100
receives data from several external data sources 500. One of the external data
sources 500 is the Network Operator information system 120 (mentioned in
Figure 1). Another external data source 500 is a server or a database, with
the
detailed descriptions in terms of features and capabilities, of all models of
mobile devices available on the market.
[0086] The pre-processing unit 510 is composed of dedicated
software executed on a computer, to process the information received from the
filtering system 110 and the external data sources 500, and update the
database 520 when necessary. As already explained, in the case of the
information transmitted by the filtering system 110 of Figure 1, the pre-
processing unit 510 queries the database 520 and an update of the database
520 is triggered by the detection of a new model of mobile device used by a
subscriber. Optionally, the pre-processing unit 510 manages roaming mobile
devices and the related updates to the database 520, to track the
corresponding models of mobile devices. A timestamp is associated with all
types of updates to the database 520, to include a time dimension in the
metrics generated by the analytic engine 530. The pre-processing unit 510 also
updates the database 520 with data related to the mobile data services usage
of the subscribers (type of mobile data service, timestamp associated to the
usage, volume of data transferred associated to a specific subscriber via its
identifier) and roaming mobile devices if desired.
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
32
[0087] The database 520 is a traditional database. It is managed by
the pre-processing unit 510 and is the source of information for the analytic
engine 530. There is a strong requirement on the performances of the
database 520 in terms of volume of data to store and computing power for the
treatment of these data, since tens of millions of subscribers may have to be
managed for large Mobile Operators.
[0088] The analytic engine 530 is the core of the analytic system
100. It is an applicative software executed on a computer, to generate the
various metrics that have been detailed in the previous sections. The
information contained in the database 520 is queried, aggregated and
processed by the analytic engine 530 to generate the metrics (essentially
various types of portfolio shares applied to models, manufacturers,
characteristics and capabilities, of mobile devices and also mobile data
services usage correlated to the models of mobile devices). Subsets of the
metrics are extracted by the reports presentation unit 540 and presented to
the
end user in the form of dashboards.
[0089] The reports presentation unit 540 consists in a Graphical
User Interface on a computer, to present different types of reports to the end
user. The reports are presented in the form of dashboards combining pre-
defined information computed by the analytic engine 530 (the reports are
generated by the analytic engine 530 and are based on the computed metrics).
A pre-defined list of reports is included by default in the analytic engine
530.
Some new reports can also be defined, using the end user control interface
550.
[0090] The end user control interface 550 also consists in a
Graphical User Interface on a computer. It offers two levels of interaction to
the
end users. Standard end users only interact with the reports presentation unit
CA 02745995 2011-06-07
WO 2010/066039 PCT/CA2009/001790
33
540, to request the generation of a report selected among the list of pre-
defined
available reports. When such a report is presented, the standard end user
interacts with the report to modify a limited number of parameters and
variables, and dynamically update the report. For instance, such a report is
the
relative portfolio share of several models of mobile devices over a time
period.
The end user has the ability to select and modify the following parameters:
the
models to be compared among a pre-defined list and the time period to
consider. The report is then automatically updated with the proper information
computed by the analytic engine 530.
[0091] Advanced end users have the same level of interaction with
the reports presentation unit 540 as the standard end users. In addition,
advanced end users are allowed to interact directly with the analytic engine
530. This capability enables an advanced end user to define a new (dynamic or
static) report that is generated by the analytic engine 530 and presented to
standard and advanced end users on the reports presentation unit 540. For this
purpose, the advanced end user selects which (dynamic) metrics are
aggregated to generate the report and the analytic engine 530 performs the
necessary computation to prepare the data that will be necessary when the
report is requested by the reports presentation unit 540. A dynamic report may
be later added to the list of pre-defined reports.
[0092] Typical end users consist in members of the marketing team
and possibly the network management team of the Mobile Operator.
[0093] Although the present method and system have been
described in the foregoing specification by means of several non-restrictive
illustrative embodiments, these illustrative embodiments can be modified at
will
within the scope, spirit and nature of the subject invention.
